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Abstracts for the February 5-7, 2015

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Abstracts for the February 5-7, 2015 ABSTRACTS FOR THE 48TH JOINT ANNUAL MEETING NEW MEXICO AND ARIZONA CHAPTERS OF THE WILDLIFE SOCIETY AND ARIZONA/NEW MEXICO CHAPTER OF THE AMERICAN FISHERIES SOCIETY FEBRUARY 5-7, 2015 HOTEL ENCANTO DE LAS CRUCES LAS CRUCES, NEW MEXICO Wildlife Best Student Paper Competition Matthew J. Gould: Ph.D. Student, Department of Biology, New Mexico State University, P.O. Box 30003, MSC 4901, Las Cruces, New Mexico 88003, [email protected]. Dr. James W. Cain III: Co-advisor, U.S. Geological Survey, New Mexico Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife and Conservation Ecology, New Mexico State University. Dr. Gary W. Roemer: Co-advisor, Department of Fish, Wildlife and Conservation Ecology, New Mexico State University. Dr. William R. Gould: Co-PI, Department of Economics, Applied Statistics, and International Business, New Mexico State University. Stewart G. Liley: Big Game Program Manager, New Mexico Department of Game and Fish. Estimating abundance and density of American black bears (Ursus americanus) in New Mexico using noninvasive genetic sampling-based capture-recapture methods Introduction One of the main challenges for a resource agency in charge of managing game species is to set harvest levels that ensure the long-term persistence of populations. Due to financial constraints, management agencies often lack relevant estimates of vital rates for all populations or subpopulations for which they must set harvest quotas. There has been recent concern and criticism voiced by different segments of the public regarding harvest objectives set for black bears (Ursus americanus) in New Mexico. Some members of the public would like to see fewer bears harvested whereas others believe that sustainable harvest levels can be increased for many populations. Our objectives were to estimate the abundance and density of black bears >1year of age in suitable bear habitat within 6 of the 14 bear management zones (BMZs) located in the Sacramento and Sangre de Cristo Mountains. Reported herein are the results for the first year of data collection in the Sangre de Cristo Mountains. Methods We sampled the population using two non-invasive genetic sampling (NGS) techniques: hair traps and bear rubs (Woods et al 1999, Kendall et al. 2008). We combined these genetic samples with tissue samples collected from bears harvested by hunters, those captured as part of research and management efforts, and bears removed by the New Mexico Department of Game and Fish (NMDGF) for depredation issues. Individual samples were genotyped across a suite of microsatellite loci. Abundance was estimated using Huggins formulation of a closed-population capture-mark-recapture model (Huggins 1989, 1991). We used Akaike’s Information Criterion (Akaike 1973) with small sample size adjustment (AICc) to rank models and used model averaging to estimate black bear abundance. Density was estimated using two approaches: in the first case we divided estimated abundance by the effective trapping area (ETA) (Dice, 1938, Wilson and Anderson, 1985), and in the second case we used a spatially explicit capture-recapture (SECR; Borchers and Efford 2008, Efford et al. 2013) model. Abundance and density estimates were generated for the northern Sangre de Cristo Mountains, the northern Sangre de Cristo Mountains excluding Vermejo Park Ranch (VPR), and VPR only. Results We sampled 256 hair traps and 46 bear rubs and collected 1,762 and 133 hair samples from each method, respectively. These hair samples were then combined with 156 tissue samples provided by NMDGF, which resulted in 2,051 genetic samples. Success rate for genotyping DNA collected from hair and tissue samples was low (40%). A total of 470 individuals were detected across all sampling methods. Estimated abundance for the northern Sangre de Cristo Mountains was 푁̂ = 1,407 (95% CI = 1,182 – 1,779). For the northern Sangre de Cristo Mountains excluding VPR, estimated abundance was 푁̂ = 1,133 (95% CI = 907 – 1,416). Estimated abundance for VPR alone was 푁̂ = 628 (95% CI = 312-1,419). Estimated density was 18.3 bears/100 km2 (95% CI = 15.4 - 23.1), 20 bears/100 km2 (95% CI = 16.4 - 25.6) and 35.8 bears/100 km2 (95% CI = 17.8 – 81.0) for these same three regions, respectively. Estimates using SECR models were higher: 21.5 bears/100 km2 (95% CI = 17.5 – 26.3), 25.2 bears/100 km2 (95% CI = 20.1 – 31.6) and 145 bears/100 km2 (95% CI = 70.8 – 298), respectively. Conclusions Capture probability was low overall because 36% of the genetic samples failed to amplify enough to generate complete genotypes. This resulted in an inability to assign samples to particular individuals, which reduced the number of new captures and recaptures. These low capture probabilities affected our estimates of abundance and density and their associated confidence intervals. The low success rate for genotyping individual samples was caused by high-levels of DNA degradation, possibly from UV radiation. Model selection suggested time was the most influential variable in our data being present in the top model for all three spatial areas. Distance to edge appeared to play a small role in the Sangre de Cristo Mountains excluding VPR. While sex appeared to only have importance in VPR’s data. Empirical studies using SECR-based methods often report lower density estimates than those produced by a traditional CMR abundance estimate divided by an ETA - e.g. ½MMDM (Obbard et al. 2010). Our density estimates using the SECR approach, however, were higher than our density estimates derived using an ETA. SECR estimates often appear less precise, and potentially misleading, because density is directly estimated from a fitted spatial model. Thus all forms of uncertainty are included, however, SECR estimators may be preferred when capture probabilities are low (Ivan et al. 2013). Our results suggest that the density of black bears in the northern Sangre de Cristo Mountains is higher than the density currently used by the NMDGF (17 bears/100 km2) to manage bears in this region. This latter value, however, falls within the 95% confidence interval estimated using the Huggins estimator for both the northern Sangre de Cristo Mountains and for the same excluding VPR but is less than the lower 95% confidence interval produced by the SECR method. Management Implication We provide density estimates that may be used to establish harvest objectives for the responsible management and future persistence of black bears in New Mexico. Literature Cited Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. Pages. 267–281. in B. N. Petrov, F. Csaki, editors. Second International Symposium on Information Theory. Akademiai Kiado, Budapest, Hungary.Borchers, D.L., and M.G. Efford. 2008. Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics 64:377-385. Borchers, D.L. and M.G. Efford. 2008. Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics 64:377-385. Dice, L.R. 1938. Some census methods for mammals. Journal of Wildlife Management 2:119-130. Efford, M.G., D.L. Borchers, and G. Mowat. 2013. Varying effort in capture-recapture studies. Methods in Ecology and Evolution 4:629-636. Huggins, R. M. 1991. Some practical aspects of a conditional likelihood approach to capture experiments. Biometrics 47:725–732. Huggins, R. M. 1989. On the statistical analysis of capture experiments. Biometrika 76:133–140. Ivan, J.S., G.C. White, and T.M. Shenk. 2013. Using simulation to compare methods for estimating density from capture-recapture data. Ecology 94:817-826. Kendall, K.C., J.B. Stetz, D.A. Roon, L.P. Waits, J.B. Boulanger, and D. Paetkau. 2008. Grizzly Bear Density in Glacier National Park, Montana. Journal of Wildlife Management 72:1693-1705. Obbard, M.E., E.J. Howe, and C.J. Kyle. 2010. Empirical comparison of density estimators for large carnivores. Journal of Applied Ecology 47:76-84. Woods, J.G., D. Paetkau, D. Lewis, B.N. McLellan, M. Proctor, and C. Strobeck. 1999. Genetic tagging of free-ranging black and brown bears. Wildlife Society Bulletin 27:616–627. Jose Griego. New Mexico Highlands University, 810 National Ave. Las Vegas, NM 87701 Email: [email protected] Advisors: Dr. Sarah Corey-Rivas, Dr. Jesus Rivas Genetic structure and hybridization of the Northern Leopard Frog (Lithobates pipiens) along the Mora Watershed in northern New Mexico The dramatic declines of the northern leopard frog, Lithobates pipiens, in the western US is a cause for concern particularly when combined with climate change altered habitats in the Southwestern US. Unfavorable environmental conditions may affect mating patterns between L. pipiens and L. blairi in zones of sympatry resulting in a higher frequency of interspecific mating opportunities (Cousineau and Rogers, 1991). DiCandia and Routman (2007) detected evidence of cytonuclear discordance between L. pipiens and L. blairi in populations residing in the Midwest regions of each species range. The nuclear marker used by DiCandia and Routman (2007) was used in another study by O’Donnell and Mock (2012) and found that the nuclear marker (FIBI7) used in the 2007 research did not show the same patterns along the Mississippi River and Great Lakes Regions. In this study, we investigate population-level connectivity of L. pipiens across a landscape of agriculture, acequias, and protected lands and potential introgression with L. blairi using nuclear (FIBI7 and microsatellites) and mitochondrial (ND1) markers. There exists a zone of sympatry between L. pipiens and L. blairi in northern New Mexico along the Mora River watershed. We collected samples (n=24) from three populations along the Mora and Sapello Rivers. Interestingly, we found cytonuclear discordance only in the Sapello River region of the watershed where frogs appear to be L. pipiens based on morphology and mtDNA, but have L. blairi FIB7 genotypes. Hybrids therefore appear to be the result of mating between L.
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